Data Engineer

AI Engineer

Company description Zenith is one of Publicis Groupe's largest media agencies, spanning 95 markets globally with US offices in New York, Burbank, Chicago, Atlanta, and Detroit. For more than 30 years, we have been at the forefront of media, helping to evolve and reshape the industry. We are highly regarded as a true agency partner by many high-profile brands for our expertise across numerous categories and wide range of capabilities that are unparalleled in the industry. Clients hire us for our track record of excellence in strategic planning, investment, and measurement. We also have specialist offerings, including content, commerce, multicultural, and future-proofed data capabilities.

Overview We are looking for an AI Engineer to lead the design, development, and deployment of AI-powered systems across the organization. This role sits at the intersection of full-stack software engineering and applied AI, with a strong emphasis on large language models (LLMs), generative AI, and production-grade ML infrastructure. You will push beyond conventional approaches to explore the advanced possibilities of AI, including autonomous agents, multi-model orchestration, and novel architectures. This is a hands-on engineering role for someone who can build reliable, scalable AI systems from prototyping and prompt engineering through to production deployment, monitoring, and iteration.

Responsibilities Design, build, and maintain production AI/ML systems, including LLM-based applications, agentic workflows, multi-model pipelines, and advanced retrieval systems Explore and prototype cutting-edge AI capabilities beyond standard patterns, such as autonomous agents, tool-use frameworks, reasoning chains, and self-improving systems Develop and optimize prompt engineering strategies, fine-tuning approaches, and evaluation frameworks for generative AI models Architect and implement MLOps pipelines for model training, versioning, deployment, and monitoring using Azure DevOps and cloud infrastructure Build robust APIs, backend services, and frontend interfaces to integrate AI capabilities into internal and customer-facing products Evaluate and integrate foundation models (OpenAI, Anthropic, Google) based on performance, cost, and latency requirements Implement guardrails, safety mechanisms, and observability tooling for deployed AI systems Collaborate with product, data, and engineering teams to identify high-impact AI use cases and translate them into technical solutions Follow engineering best practices, including code review, testing, CI/CD, and documentation for AI codebases Stay current with the rapidly evolving AI landscape and drive adoption of emerging tools, frameworks, and techniques

Qualifications Required Technical Skills Strong software engineering fundamentals in Python and JavaScript, with experience in production-grade application development Proficiency in modern JavaScript frameworks such as React and Angular for building AI-powered user interfaces and tooling Deep hands-on experience with LLMs, prompt engineering, and generative AI frameworks (LangChain, LlamaIndex, or similar) Experience with advanced AI patterns beyond basic RAG, including autonomous agents, multi-step reasoning, tool use, and multi-model orchestration Proficiency in MLOps tooling and practices: model serving (e.g., vLLM, TGI), experiment tracking, and CI/CD for ML Strong experience with Azure DevOps for pipeline management, release automation, and infrastructure-as-code Solid knowledge of cloud infrastructure, networking, and security on AWS and Azure Hands-on experience with AWS services including S3, Lambda, EC2, and related compute and storage offerings Working knowledge of relational and specialized databases, including PostgreSQL, Amazon Redshift, Qdrant (vector database), and Redis Experience with containerized deployments (Docker, Kubernetes) and infrastructure automation Solid understanding of software architecture patterns, API design (REST/GraphQL), and distributed systems Experience with model evaluation, A/B testing, and performance benchmarking for AI systems What sets you apart You build AI systems that are production-ready from day one: reliable, observable, and maintainable You think beyond off-the-shelf solutions and are excited to explore what is newly possible with AI You think in terms of architecture and scalability, not just proof-of-concept demos You proactively identify failure modes, edge cases, and safety considerations before they reach production You can operate independently while collaborating effectively across engineering, product, and data teams You communicate complex technical concepts clearly to both technical and non-technical stakeholders